DreamQAS: Learning a Decision-Useful World Model for VQE-Efficient Quantum Architecture Search

• URL: http://arxiv.org/abs/2607.29491v1
• Date: Fri, 31 Jul 2026 14:58:23 GMT
• Title: DreamQAS: Learning a Decision-Useful World Model for VQE-Efficient Quantum Architecture Search
• Abstract summary: Reinforcement-learning-based quantum architecture search (RL-QAS) repeatedly optimize a variational quantum eigensolver (VQE) after extending a circuit.<n>We introduce DreamQAS, a model-based RL framework that preserves exact circuit dynamics and learns only the expensive post-VQE feedback.
• Score: 9.679776057194056
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Reinforcement-learning-based quantum architecture search (RL-QAS) repeatedly optimizes a variational quantum eigensolver (VQE) after extending a circuit, although circuit construction and action legality are deterministic and known. We introduce DreamQAS, a model-based RL framework that preserves these exact circuit dynamics and learns only the expensive post-VQE feedback. A recurrent randomized-prior ensemble predicts an oracle-free score relative to an empirical energy frontier and supports multi-step imagined policy learning over explicit legal circuits. Ranking-based activation, uncertainty-aware pessimism and truncation, and selective real-VQE verification form a reliability-controlled learning loop. Under a common 15,000-episode budget and frozen evaluation for the RL methods, DreamQAS has the lowest mean frozen-policy energy error on four of five molecular tasks and the second-lowest on one. At fine-error targets reached by all seeds of both methods, it uses 1.6x to 2.0x fewer real VQE calls on four tasks and 10.6x fewer on BeH2-8q. Counterfactual action-ranking utility increases across all five tasks, with a mean increase of 0.346 and a 95 percent confidence interval of [0.185, 0.507], while direct greedy and beam use of the same model does not recover the gains of imagined policy learning. Ensemble disagreement also improves risk-coverage over random rejection on all three probed tasks. These results establish a world-model design for QAS whose value lies in decision-useful feedback rather than exact energy prediction.

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